DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant’s submission filed on 02/05/2026 and 02/20/2026 has been entered.
Response to Amendment
The amendment filed 02/05/2026 is being entered. Claims 1 and 11 are amended. Claim 9 and 17-19 are canceled. Claim 20-21 are new claim. Claims 1-8, 10-16, and 20-21 are pending, and rejected as detailed below.
Response to Arguments
Anticipation
Applicant argues that independent claim 1, as mended herein, recites “the heavy machine is a heavy-duty vehicle specially designed for executing construction tasks” and further that “the heavy machine includes a drivetrain configured to provide propulsion to the heavy machine and further includes a tool maneuverable by one or more actuators independently of the propulsion of the heavy machine.” Abbott discloses a handheld power tool (such as a drill, impact driver, or reciprocating saw). Abbott lacks any disclosure of a heavy-duty vehicle. Abbott also lacks any teaching of a propulsion drivetrain or a tool maneuverable by separate actuators independent of vehicle movement. Claim 1 is not anticipated for at least these reasons. Claim 11 includes a similar combination of elements relative to the above-discussed elements of claim 1 and is not anticipated by Abbott for substantially the same reasons.
Applicant’s arguments, as amended herein, with respect to the rejections of claims 1 and 11 under 35 U.S.C. §102 have been fully considered and persuasive. Therefore, the 35 U.S.C. §102 rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection for claims 1 and 11 under 35 U.S.C. §102 is made in view of newly found reference GARCIA (WO 2018203091 A1). In particular, the amendments to claims 1 and 11 are addressed in the instant office action.
Applicant argues that the “randomly ignore” claims (5 and 12), Abbott merely discloses varying how quickly Abbott’s machine learning controller reacts to data. See Abbott at ¶ 116. Nothing in Abbott suggests that pieces of data would be ignored, let alone randomly ignored. Therefore, claims 5 and 12 should be allowed for this additional reason.
Applicant’s arguments with respect to that Abbott does not teach randomly ignoring data, as in claims 5 and 12 have been fully considered and persuasive. More specifically, the specification of the instant application disclosed that certain pieces of data is ignored randomly to improve the efficiency of the machine learning controller based on the importance of a particular piece of data (0034; “One aspect of this disclosure that leads to increased efficiency of the artificial intelligence module 18 is that the artificial intelligence module 18 randomly ignores certain pieces of the data. By randomly ignoring certain pieces of data, the artificial intelligence module 18 more efficiently determines the importance of a particular piece of data. Because the artificial intelligence module 18 is so efficient, it is not necessary to embody the artificial intelligence module 18 on a large computer, such as a server or a separate, high-powered computer on the heavy machine 12. Rather, the artificial intelligence module 18 can run on an existing, relatively low-powered computer, such as those that are already part of most traditional heavy machines.”). In other words, the importance of data has to be specified or identified within the machine learning controller so that one or more piece of data that is irrelevant or ineffective for the desired outcome can be ignored. Therefore, the 35 U.S.C. §102 rejection for claims 5 and 12 has been withdrawn. However, upon further consideration, a new ground(s) of rejection for claims 5 and 12 under 35 U.S.C. §103 is made in view of newly found reference GARCIA (WO 2018203091 A1) and Chethan Babu V. (US 20210182602 A1). In particular, the amendments to claims 5 and 12 are addressed in the instant office action as Chethan Babu V. teaches how missing at random data can classified within machine learning process (Chethan Babu V.; 0032).
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3, 11, 16, and 20-21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by GARCIA (WO 2018203091 A1).
Regarding claim 1, GARCIA teaches (Currently Amended) A method (GARCIA, page 1, para. 1; “The present invention relates to a working machine. In particular, the present invention relates to determining a position of a moveable element of a working machine.”), comprising:
collecting data indicative of the manner in which an operator performs tasks (GARCIA, page 7, para. 3; “The method may comprise capturing, using the camera, a plurality of images of at least a portion of the moveable element; and determining, using the sensor, sensor data which corresponds to each image of the plurality of images, wherein the sensor data indicates the position of the moveable element in each image.”) using a heavy machine (GARCIA, page 3, para. 2; “The working machine may be one of: construction and agricultural machinery, with digging or materials handling capabilities, including but not limited to a backhoe loader; a tractor; a forklift; a skid steer loader; an excavator; and a telescopic handler.”), wherein the heavy machine is a heavy-duty vehicle specially designed for executing construction tasks, wherein the heavy machine includes a drivetrain configured to provide propulsion to the heavy machine (GARCIA, page 1, para. 2; “a backhoe loader has a shovel on the front and a backhoe on the rear. When performing certain tasks, such as trenching, it can be useful to know an accurate position of the moveable element.”, it is inherent vehicle is equipped with an engine and drivable) and further includes a tool maneuverable by one or more actuators independently of the propulsion of the heavy machine (GARCIA, page 1, para. 3; “To determine the position of a moveable element, it is common to derive the position from one or more sensors. A sensor may detect the extension of a hydraulic actuator driving the moveable element. For example, a sensor can measure the extension of a hydraulic actuator which drives an individual section of a backhoe loader arm, or a hydraulic actuator which pivots a bucket. Alternatively, a sensor may detect rotation between individual sections of the moveable element.”);
analyzing the data with an artificial intelligence module (GARCIA, page 2, para. 3; “a processor configured to execute a machine learning algorithm trained to determine a position of a moveable element from an image of the moveable element, wherein the machine learning algorithm receives the image from the camera and determines the position of the moveable element in the image.”); and
controlling at least some components of the heavy machine in response to instructions from the artificial intelligence module to perform at least some tasks of the heavy machine (GARCIA, page 3, para. 5; “The machine learning algorithm is able to predict the position of one or more points on the attachment. The attachment may, for example, be a tool (such as a breaker, a blade, or a cutter), a bucket, or forks. The machine learning algorithm may predict the position of a point or edge at which the attachment contacts a workpiece (such as the edge of a bucket or the tip of a breaker).”), wherein the controlling step includes maneuvering the tool of the heavy machine (GARCIA, page 3, para. 5; “This may be used to provide guidance to assist an operator to position the attachment with respect to the workpiece (such as the ground) for precision operations (such as, digging a trench of a desired depth) or to permit fully automatic control of the attachment.”) and does not include driving the heavy machine (GARCIA, page 3, para. 5; “This may be used to provide guidance to assist an operator to position the attachment with respect to the workpiece (such as the ground) for precision operations (such as, digging a trench of a desired depth) or to permit fully automatic control of the attachment.”, it is obvious and inherent when the backhoe attachment is used to dig the trench, the vehicle is not drivable as the stabilizer arms are deployed. Furthermore, it is clearly stated that the machine learning algorithm is only used to determine the position of the movable element).
Regarding claim 2, GARCIA teaches (Original) The method as recited in claim 1, wherein the artificial intelligence module is not cloud based and exists on a controller of the heavy machine (GARCIA, page 15, Brief Description of the Drawings; “Figure 4 illustrates a training machine which generates training data for training the neural network used by the backhoe loader of Figure 2;”).
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Regarding claim 3, GARCIA teaches (Original) The method as recited in claim 1, wherein the artificial intelligence module includes a neural network (GARCIA, page 2, para. 6; “The machine learning algorithm may comprise a neural network. A neural network has been found to be particularly accurate at determining the position of the moveable element based on images.”).
Regarding claim 11, GARCIA teaches (Currently Amended) A heavy machine (GARCIA, page 1, para. 1; “The present invention relates to a working machine. In particular, the present invention relates to determining a position of a moveable element of a working machine.”), comprising:
a drivetrain configured to facilitate propulsion of the heavy machine (GARCIA, page 1, para. 2; “a backhoe loader has a shovel on the front and a backhoe on the rear. When performing certain tasks, such as trenching, it can be useful to know an accurate position of the moveable element.”, it is inherent vehicle is equipped with an engine and drivable);
a controller including an artificial intelligence module (GARCIA, page 2, para. 3; “a processor configured to execute a machine learning algorithm trained to determine a position of a moveable element from an image of the moveable element, wherein the machine learning algorithm receives the image from the camera and determines the position of the moveable element in the image.”), wherein the controller is configured to receive data from at least one component of the heavy machine indicative of the manner in which an operator performs tasks of the heavy machine (GARCIA, page 2, para. 3; “a processor configured to execute a machine learning algorithm trained to determine a position of a moveable element from an image of the moveable element, wherein the machine learning algorithm receives the image from the camera and determines the position of the moveable element in the image.”), wherein the data is configured to be analyzed by the artificial intelligence module, and wherein the artificial intelligence module is configured to cause the controller to issue instructions to at least some components of the heavy machine to perform at least some tasks of the heavy machine (GARCIA, page 3, para. 5; “The machine learning algorithm is able to predict the position of one or more points on the attachment. The attachment may, for example, be a tool (such as a breaker, a blade, or a cutter), a bucket, or forks. The machine learning algorithm may predict the position of a point or edge at which the attachment contacts a workpiece (such as the edge of a bucket or the tip of a breaker).”); and
a tool, wherein the controller is configured to issue instructions to maneuver the tool (GARCIA, page 3, para. 5; “The machine learning algorithm is able to predict the position of one or more points on the attachment. The attachment may, for example, be a tool (such as a breaker, a blade, or a cutter), a bucket, or forks. The machine learning algorithm may predict the position of a point or edge at which the attachment contacts a workpiece (such as the edge of a bucket or the tip of a breaker).”) but is not configured to issue instructions to drive the heavy machine, wherein the tool is maneuverable independent of propulsion of the heavy machine (GARCIA, page 3, para. 5; “This may be used to provide guidance to assist an operator to position the attachment with respect to the workpiece (such as the ground) for precision operations (such as, digging a trench of a desired depth) or to permit fully automatic control of the attachment.”, it is obvious and inherent when the backhoe attachment is used to dig the trench, the vehicle is not drivable as the stabilizer arms are deployed. Furthermore, it is clearly stated that the machine learning algorithm is only used to determine the position of the movable element),
wherein the heavy machine is a heavy-duty vehicle specially designed for executing construction tasks (GARCIA, page 3, para. 2; “The working machine may be one of: construction and agricultural machinery, with digging or materials handling capabilities, including but not limited to a backhoe loader; a tractor; a forklift; a skid steer loader; an excavator; and a telescopic handler.”).
Regarding claim 16, GARCIA teaches (Original) The heavy machine as recited in claim 11, wherein the artificial intelligence module includes a neural network (GARCIA, page 2, para. 6; “The machine learning algorithm may comprise a neural network. A neural network has been found to be particularly accurate at determining the position of the moveable element based on images.”).
Regarding claim 20, GARCIA teaches (New) The method as recited in claim 1, wherein the heavy machine is a backhoe loader, front loader, or a bulldozer (GARCIA, page 3, para. 2; “The working machine may be one of: construction and agricultural machinery, with digging or materials handling capabilities, including but not limited to a backhoe loader; a tractor; a forklift; a skid steer loader; an excavator; and a telescopic handler.”).
Regarding claim 21, GARCIA teaches (New) The heavy machine as recited in claim 11, wherein the heavy machine is a backhoe loader, front loader, or a bulldozer (GARCIA, page 3, para. 2; “The working machine may be one of: construction and agricultural machinery, with digging or materials handling capabilities, including but not limited to a backhoe loader; a tractor; a forklift; a skid steer loader; an excavator; and a telescopic handler.”).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 4, 6-8, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over GARCIA (WO 2018203091 A1) as applied to claim1 and 11 above, respectively, and further in view of Abbott (US 20190227528 A1).
Regarding claim 4, GARCIA teaches, (Original) The method as recited in claim 1, wherein:
the artificial intelligence module (GARCIA, page 2, para. 3; “a processor configured to execute a machine learning algorithm trained to determine a position of a moveable element from an image of the moveable element, wherein the machine learning algorithm receives the image from the camera and determines the position of the moveable element in the image.”) includes a first layer configured to receive the data, a second long-short term memory layer, a third long short-term memory layer, and a fourth layer configured to generate an output, and
the instructions from the artificial intelligence module are based on the output of the fourth layer.
GARCIA does not explicitly teach the artificial intelligence module includes a first layer configured to receive the data, a second long-short term memory layer, a third long short-term memory layer, and a fourth layer configured to generate an output, and
the instructions from the artificial intelligence module are based on the output of the fourth layer.
Abbott, in the same field of endeavor (Abbott, at least one para. 0003; “In one embodiment, a power tool is provided including a motor, a sensor, and an electronic control assembly having a machine learning controller including an electronic processor and a memory.”) teaches the artificial intelligence module includes a first layer configured to receive the data, a second long-short term memory layer, a third long short-term memory layer, and a fourth layer configured to generate an output (Abbott, at least one para. 0055; “In one example, the machine learning controller 120 implements an artificial neural network. The artificial neural network typically includes an input layer, a plurality of hidden layers or nodes, and an output layer.” and Abbott, at least one para. 0150; “In particular, the machine learning controller 540 implements a long-short-term-memory (LSTM) recurrent neural network.”), and
the instructions from the artificial intelligence module are based on the output of the fourth layer (Abbott, at least one para. 0104; “For example, when the machine learning control 585 is configured to identify a type of fastener used with the power tool 500, the machine learning control 585 may determine that since the last three operations used lug nuts, the fourth operation is also likely to use a lug nut.”).
GARCIA and Abbott are both considered to be analogous to the claimed invention because both of them are in the same field as machine learning algorithm to control a specific element of the machine as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have modified the machine learning algorithm of the GARCIA with teaching of Abbott. One of the ordinary skill in the art would have been motivated to make this modification because the claim would have been obvious because the substitution of one known element (machine learning controller of Abbott) for another (machine learning controller of GARCIA) would have yielded predictable results to one of ordinary skill in the art. Furthermore, the claim would have been obvious because the technique for improving a particular class of devices (methods or products) was part of the ordinary capabilities of one skilled in the art, in view of the teaching of the technique for improvement in other situations.
Regarding claim 6, GARCIA teaches, (Original) The method as recited in claim 1 (GARCIA, page 2, para. 3; “a processor configured to execute a machine learning algorithm trained to determine a position of a moveable element from an image of the moveable element, wherein the machine learning algorithm receives the image from the camera and determines the position of the moveable element in the image.”), further comprising: predicting, based on the data, a task that should be performed.
GARCIA does not explicitly teach further comprising: predicting, based on the data, a task that should be performed.
Abbott, in the same field of endeavor (Abbott, at least one para. 0003; “In one embodiment, a power tool is provided including a motor, a sensor, and an electronic control assembly having a machine learning controller including an electronic processor and a memory.”) teaches further comprising: predicting, based on the data, a task that should be performed (Abbott, at least one para. 0087; “In such embodiments, a user may select an operating mode for the power tool 500 based on, for example, a number of actuations of the mode pad 527. For example, when the user activates the actuator three times, the power tool 500 may operate in a third operating mode.”, wherein the task is identified as selection of the third operating mode), and additionally, (Abbott, at least one para. 0062; “In some embodiments, the power tool 105 periodically transmits the usage data to the server 110 based on a predetermined schedule (e.g., every eight hours).”, wherein the task is Data transmission).
GARCIA and Abbott are both considered to be analogous to the claimed invention because both of them are in the same field as machine learning algorithm to control a specific element of the machine as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have modified the machine learning algorithm of the GARCIA with teaching of Abbott. One of ordinary skill in the art would have been capable of applying a known technique to a known device (method, or product) that was ready for improvement, and the results would have been predictable to one of ordinary skill in the art. Furthermore, the claim would have been obvious because the technique for improving a particular class of devices (methods or products) was part of the ordinary capabilities of one skilled in the art, in view of the teaching of the technique for improvement in other situations.
Regarding claim 7, Abbott teaches, (Original) The method as recited in claim 6, further comprising: predicting, based on the data, a time when the predicted task should be performed (Abbott, at least one para. 0087; “In such embodiments, a user may select an operating mode for the power tool 500 based on, for example, a number of actuations of the mode pad 527. For example, when the user activates the actuator three times, the power tool 500 may operate in a third operating mode.”, wherein the time is identified as the number of actuations that is three), and additionally, (Abbott, at least one para. 0062; “In some embodiments, the power tool 105 periodically transmits the usage data to the server 110 based on a predetermined schedule (e.g., every eight hours).”, wherein the time is identified as every eight hours).
Regarding claim 8, Abbott teaches, (Original) The method as recited in claim 7, wherein the controlling step includes performing the predicted task at the predicted time (Abbott, at least one para. 0087; “In such embodiments, a user may select an operating mode for the power tool 500 based on, for example, a number of actuations of the mode pad 527. For example, when the user activates the actuator three times, the power tool 500 may operate in a third operating mode.”, wherein predicted task is operation of the power tool on the third operating mode), and additionally, (Abbott, at least one para. 0062; “In some embodiments, the power tool 105 periodically transmits the usage data to the server 110 based on a predetermined schedule (e.g., every eight hours).”, wherein predicted task is data transmission to the server).
Regarding claim 15, GARCIA teaches, (Original) The heavy machine as recited in claim 11 (GARCIA, page 2, para. 3; “a processor configured to execute a machine learning algorithm trained to determine a position of a moveable element from an image of the moveable element, wherein the machine learning algorithm receives the image from the camera and determines the position of the moveable element in the image.”), further comprising: a push button configured to cause the artificial intelligence module to perform a learned function.
GARCIA does not explicitly teach further comprising: a push button configured to cause the artificial intelligence module to perform a learned function.
Abbott, in the same field of endeavor (Abbott, at least one para. 0003; “In one embodiment, a power tool is provided including a motor, a sensor, and an electronic control assembly having a machine learning controller including an electronic processor and a memory.”) teaches further comprising: a push button configured to cause the artificial intelligence module to perform a learned function (Abbott, at least one para. 0187; “Further, in some embodiments, the reciprocating saw 1650 includes a plurality of selectable operating mode profiles, each specifying one or more of, for example, a particular motor speed, torque, current, ramp up period, breakthrough threshold, and the like. To select between the operating mode profiles, the reciprocating saw 1650 may include a push button, a rotary dial, or the like, to allow a user to provide a selection input signal to the electronic processor 550. In some embodiments, one such selectable operating mode profile is referred to as a machine learning profile that, when enabled, causes the reciprocating saw 1650 to implement the method 1600 of FIG. 27. ”).
GARCIA and Abbott are both considered to be analogous to the claimed invention because both of them are in the same field as machine learning algorithm to control a specific element of the machine as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have modified the machine learning algorithm of the GARCIA with teaching of Abbott. The claim would have been obvious because all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art at the time of the invention. Furthermore, One of ordinary skill in the art would have been capable of applying a known technique to a known device (method, or product) that was ready for improvement, and the results would have been predictable to one of ordinary skill in the art.
Claim(s) 5 is rejected under 35 U.S.C. 103 as being unpatentable over GARCIA (WO 2018203091 A1) and Abbott (US 20190227528 A1) as applied to claim 4, and further in view of Chethan Babu V (US 20210182602 A1).
Regarding claim 5, Abbott teaches (Previously Presented) The method as recited in claim 4, wherein the artificial intelligence module (Abbott, at least one para. 0055; “In one example, the machine learning controller 120 implements an artificial neural network. The artificial neural network typically includes an input layer, a plurality of hidden layers or nodes, and an output layer.” and Abbott, at least one para. 0150; “In particular, the machine learning controller 540 implements a long-short-term-memory (LSTM) recurrent neural network.”) is configured to ignore one or more pieces of the data at random.
The combination of GARCIA and Abbott does not explicitly teach the artificial intelligence module is configured to ignore one or more pieces of the data at random.
Chethan Babu V, in the same field of endeavor (Chethan Babu V, at least one para. 0001; “The field generally relates to data analytics and data science, and specifically to item imputation for missing values.”) teaches the artificial intelligence module is configured to ignore one or more pieces of the data at random (Chethan Babu V, at least one para. 0032; “Missing data can be classified into three categories: (i) missing at random (MAR), not missing at random (NMAR) and missing completed at random (MCAR). In MAR, data that is missing is not related to the missing data; somehow it is related too some of the observed data. In NMAR, missing data directly or indirectly depend on the value of some other variable in the data set. In MCAR, the missing data is order less. Such missing values have the lowest possibility or no possibility of finding patterns. In reality, MCAR is the most prevalent type of missing data.”).
The combination of GARCIA, Abbott, and Chethan Babu V are considered to be analogous to the claimed invention because all of them are in the same field as optimizing machine learning algorithm as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have modified the machine learning algorithm of the Abbott with teaching of Chethan Babu V. The claim would have been obvious because all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art at the time of the invention. Furthermore, One of ordinary skill in the art would have been capable of applying a known technique to a known device (method, or product) that was ready for improvement, and the results would have been predictable to one of ordinary skill in the art.
Claim(s) 10 is rejected under 35 U.S.C. 103 as being unpatentable over GARCIA (WO 2018203091 A1) as applied to claim 1, and further in view of Li (WO 2019189888 A1).
Regarding claim 10, GARCIA teaches (Original) The method as recited in claim 1, wherein: the controlling step (GARCIA, page 2, para. 3; “The machine learning algorithm is able to predict the position of one or more points on the attachment. The attachment may, for example, be a tool (such as a breaker, a blade, or a cutter), a bucket, or forks. The machine learning algorithm may predict the position of a point or edge at which the attachment contacts a workpiece (such as the edge of a bucket or the tip of a breaker).”) includes limiting engine rotation such that a speed of the engine does not exceed a threshold, and the threshold is determined in the analyzing step.
GARCIA does not explicitly teach limiting engine rotation such that a speed of the engine does not exceed a threshold, and the threshold is determined in the analyzing step.
Li, in the same field of endeavor (Li, Technical-field; “The present invention relates to a construction machine operation support system and the like.”) teaches limiting engine rotation such that a speed of the engine does not exceed a threshold (Li, Configuration of excavator; “The ECU 74 controls various actuators (for example, a fuel injection device, etc.) of the engine 11 in accordance with a control command from the controller 30, and makes the engine 11 rotate at a set target rotation speed (set rotation speed) (constant rotation). Rotation control). At this time, the ECU 74 performs constant rotation control of the engine 11 based on the rotation speed of the engine 11 detected by the engine rotation speed sensor 11a.”), and the threshold is determined in the analyzing step (Li, Configuration of excavator; “the controller 30 sets the target rotation speed based on a work mode or the like set in advance by a predetermined operation by an operator or the like, and outputs a control command to the ECU 74, thereby rotating the engine 11 at a constant speed via the ECU 74.”).
The combination of GARCIA and Li are considered to be analogous to the claimed invention because all of them are in the same field as optimizing machine learning algorithm as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have modified the machine learning algorithm of the GARCIA with teaching of Li. The claim would have been obvious because all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art at the time of the invention. Furthermore, it is obvious and inherent that the drivers always have to set the engine rotation of the engine according to a threshold so that the backhoe attachment can be operated.
Claim(s) 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over GARCIA (WO 2018203091 A1) as applied to claim 11, and further in view of Chethan Babu V (US 20210182602 A1).
Regarding claim 12, GARCIA teaches (Previously Presented) The heavy machine as recited in claim 11, wherein the artificial intelligence module (GARCIA, page 2, para. 3; “a processor configured to execute a machine learning algorithm trained to determine a position of a moveable element from an image of the moveable element, wherein the machine leaning algorithm receives the image from the camera and determines the position of the moveable element in the image.”) is configured to ignore one or more pieces of the data at random.
GARCIA does not explicitly teach the artificial intelligence module is configured to ignore one or more pieces of the data at random.
Chethan Babu V, in the same field of endeavor (Chethan Babu V, at least one para. 0001; “The field generally relates to data analytics and data science, and specifically to item imputation for missing values.”) teaches the artificial intelligence module is configured to ignore one or more pieces of the data at random (Chethan Babu V, at least one para. 0032; “Missing data can be classified into three categories: (i) missing at random (MAR), not missing at random (NMAR) and missing completed at random (MCAR). In MAR, data that is missing is not related to the missing data; somehow it is related too some of the observed data. In NMAR, missing data directly or indirectly depend on the value of some other variable in the data set. In MCAR, the missing data is order less. Such missing values have the lowest possibility or no possibility of finding patterns. In reality, MCAR is the most prevalent type of missing data.”).
The combination of GARCIA and Chethan Babu V are considered to be analogous to the claimed invention because all of them are in the same field as optimizing machine learning algorithm as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have modified the machine learning algorithm of the GARCIA with teaching of Chethan Babu V. The claim would have been obvious because all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art at the time of the invention. Furthermore, One of ordinary skill in the art would have been capable of applying a known technique to a known device (method, or product) that was ready for improvement, and the results would have been predictable to one of ordinary skill in the art.
Regarding claim 13, GARCIA teaches, (Original) The heavy machine as recited in claim 12, wherein the artificial intelligence module (GARCIA, page 2, para. 3; “a processor configured to execute a machine learning algorithm trained to determine a position of a moveable element from an image of the moveable element, wherein the machine learning algorithm receives the image from the camera and determines the position of the moveable element in the image.”) includes a first layer configured to receive the data, a second long-short term memory layer, a third long short-term memory layer, and a fourth layer configured to generate an output.
GARCIA does not explicitly teach the artificial intelligence module includes a first layer configured to receive the data, a second long-short term memory layer, a third long short-term memory layer, and a fourth layer configured to generate an output.
Abbott, in the same field of endeavor (Abbott, at least one para. 0003; “In one embodiment, a power tool is provided including a motor, a sensor, and an electronic control assembly having a machine learning controller including an electronic processor and a memory.”) teaches the artificial intelligence module includes a first layer configured to receive the data, a second long-short term memory layer, a third long short-term memory layer, and a fourth layer configured to generate an output (Abbott, at least one para. 0055; “In one example, the machine learning controller 120 implements an artificial neural network. The artificial neural network typically includes an input layer, a plurality of hidden layers or nodes, and an output layer.” and Abbott, at least one para. 0150; “In particular, the machine learning controller 540 implements a long-short-term-memory (LSTM) recurrent neural network.”).
GARCIA and Abbott are both considered to be analogous to the claimed invention because both of them are in the same field as machine learning algorithm to control a specific element of the machine as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have modified the machine learning algorithm of the GARCIA with teaching of Abbott. One of the ordinary skill in the art would have been motivated to make this modification because the claim would have been obvious because the substitution of one known element (machine learning controller of Abbott) for another (machine learning controller of GARCIA) would have yielded predictable results to one of ordinary skill in the art. Furthermore, the claim would have been obvious because the technique for improving a particular class of devices (methods or products) was part of the ordinary capabilities of one skilled in the art, in view of the teaching of the technique for improvement in other situations.
Regarding claim 14, GARCIA teaches (Original) The heavy machine as recited in claim 13, wherein the artificial intelligence module is not cloud based and exists on a controller of the heavy machine (GARCIA, page 15, Brief Description of the Drawings; “Figure 4 illustrates a training machine which generates training data for training the neural network used by the backhoe loader of Figure 2;”).
Conclusion
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/U.P.C./Examiner, Art Unit 3665 /CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665